Jorge Veiga Fachal

PhD candidate

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About Me

I am a PhD candidate currently working on distributed computation of Big Data workloads. My main research interests cover related technologies, while also including topics like Cloud Computing and High Performance Computing.

Jorge Veiga, Guillermo L. Taboada, Xoán C. Pardo, and Juan Touriño
"The HPS3 service: reduction of cost and transfer time for storing data on clouds"16th IEEE International Conference on High Performance Computing and Communications (HPCC'14)
Paris, France, August 2014.PreprintOnline

Projects

BDEv

BDEv is a tool to evaluate Big Data processing solutions in terms of performance and resource efficiency. It includes several ready-to-use frameworks (e.g. Hadoop, Spark, Flink) and manages the configuration needed to leverage the available computational resources, like CPU, memory and network interfaces. The evaluation of these frameworks can be done by using different benchmarks (e.g. TeraSort, WordCount) included in the BDEv distribution, while also enabling the execution of custom commands. Moreover, BDEv eases the execution of experiments and the task of recovering results by providing automatically generated graphs.

Flame-MR

Flame-MR is a MapReduce framework which improves the performance of Hadoop applications. It employs several kinds of optimizations, like avoidance of memory copies, efficient sort and merge algorithms and flexible use of resources. Moreover, its event-driven architecture overlaps the data transferring and processing. Flame-MR also keeps binary compatibility with Hadoop, so applications do not have to be modified or recompiled to be executed. The experimental results show that Flame-MR can reduce the execution time of iterative workloads by a half.

MarDRe

MarDRe is a de novo MapReduce-based parallel tool to remove duplicate and near-duplicate DNA reads in large scale FASTQ/FASTA datasets. Duplicate reads can be seen as identical or nearly identical sequences with some mismatches, so removing them decreases memory requirements and computational time of downstream analysis, without damaging biological information. MarDRe is written in Java and built upon Apache Hadoop.